Department of Psychology, Cognitive Neuroscience Research Unit, City, University of London, UK.
Department of Psychology, Cognitive Neuroscience Research Unit, City, University of London, UK.
Cogn Psychol. 2020 Nov;122:101321. doi: 10.1016/j.cogpsych.2020.101321. Epub 2020 Jun 24.
Decision-making is a fundamental human activity requiring explanation at the neurocognitive level. Current theoretical frameworks assume that, during sensory-based decision-making, the stimulus is sampled sequentially. The resulting evidence is accumulated over time as a decision variable until a threshold is reached and a response is initiated. Several neural signals, including the centroparietal positivity (CPP) measured from the human electroencephalogram (EEG), appear to display the accumulation-to-bound profile associated with the decision variable. Here, we evaluate the putative computational role of the CPP as a model-derived accumulation-to-bound signal, focussing on point-by-point correspondence between model predictions and data in order to go beyond simple summary measures like average slope. In two experiments, we explored the CPP under two manipulations (namely non-stationary evidence and probabilistic decision biases) that complement one another by targeting the shape and amplitude of accumulation respectively. We fit sequential sampling models to the behavioural data, and used the resulting parameters to simulate the decision variable, before directly comparing the simulated profile to the CPP waveform. In both experiments, model predictions deviated from our naïve expectations, yet showed similarities with the neurodynamic data, illustrating the importance of a formal modelling approach. The CPP appears to arise from brain processes that implement a decision variable (as formalised in sequential-sampling models) and may therefore inform our understanding of decision-making at both the representational and implementational levels of analysis, but at this point it is uncertain whether a single model can explain how the CPP varies across different kinds of task manipulation.
决策是一种基本的人类活动，需要从神经认知层面进行解释。当前的理论框架假设，在基于感觉的决策过程中，刺激是顺序采样的。随着时间的推移，作为决策变量的证据会不断积累，直到达到阈值并引发反应。几个神经信号，包括从中获取的中央顶区正波（CPP），似乎显示出与决策变量相关的累积到边界的特征。在这里，我们评估 CPP 作为一种模型衍生的累积到边界信号的潜在计算作用，重点关注模型预测和数据之间的逐点对应关系，以超越简单的汇总措施，如平均斜率。在两项实验中，我们探索了 CPP 在两种操纵下的情况（即非平稳证据和概率决策偏差），它们通过分别针对累积的形状和幅度来互补。我们将顺序采样模型拟合到行为数据中，并使用得到的参数来模拟决策变量，然后直接将模拟的特征与 CPP 波形进行比较。在两项实验中，模型预测与我们的朴素预期存在偏差，但与神经动力学数据存在相似之处，这说明了正式建模方法的重要性。CPP 似乎源于实现决策变量的大脑过程（如在顺序采样模型中形式化），因此可能为我们在表示和实现分析的决策水平上提供信息，但目前尚不确定单个模型是否可以解释 CPP 在不同任务操作中的变化方式。
